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Sydney Morning HeraldLet's curb our 3D-printer enthusiasmSydney Morning HeraldThat is the way exponential technologies usually go. Expectations get raised when people first read about a technological breakthrough.

Astronomy could be the first discipline in which the rate of discovery by machines outpaces humans’ ability to interpret it.

“In twenty years time, it is likely that most astronomers will never go near a cutting-edge telescope,” says Ray Norris at the Commonwealth Scientific and Industrial Research Organisation in Epping, Australia. So begins a fascinating discussion about the future of humanity’s oldest science.

Norris paints an optimistic picture. For him, the future is filled with automation that will make astronomers’ jobs easier. He says, for example, that in twenty years time: “I expect to be able to click on an object in a paper, and see its image at all wavelengths.” This data will be provided more or less automatically by a new generation of smart telescopes that calibrate and edit data on the fly and then send it to a Virtual Observatory that anybody can access.

The job for astronomers will be to theorise about this data, to look for patterns within it and to see how it explains some problems and creates others. They might then suggest what other data to collect. That should free up much of their time. Norris says the time not spent fiddling with equitorial mounts and lens cloths will allow them up to better engage with the public who pay their wages.

That’s certainly a reasonable change from what astronomers do today but has Norris gone far enough? One thing he fails to take into account is the newfound ability of computers to analyse data in ways entirely inaccessible to humans. Last year, Hod Lipson and pals at Cornell University developed a genetic algorithm capable of sifting through data looking for the laws of physics behind it.

And it seems to work. These guys generated a load of data by tracking the motion of things like simple harmonic oscillators and chaotic double-pendulums. They then set their algorithm loose on the raw data–not the manicured stuff but the warts’n’all measurements.

Their jaw-dropping result is that their algorithm derived Newton’s laws of motion from this data, without outside help. Since then, they’ve been inundated with requests to let their algorithm loose on other data sets. They’ve even set up a website where anybody can try it for themselves.

That’s quite an eye-opener. One problem is that the algorithm doesn’t always throw up well known results like Newton’s laws. And that leaves scientists puzzling over the mathematical relations that it reveals. What do they mean? How should they be interpreted? Are they important?

This should be of more than passing interest to astronomers. As Norris points out, astronomers are in the process of automating their work, to the point where the only task left to them is to analyse the data. And yet, Lipson’s work at Cornell indicates that even this can be automated too.

What Norris has failed to take into account is what will happen when Lipson’s algorithm, or something like it, is set to work on the corpus of data in the Virtual Observatory. The likelihood is that these algorithms will become powerful tools for discovering relationships in data that humans would find difficult to extract. That leaves astronomers with the task of puzzling over the results, sometimes understanding them but perhaps more often, not knowing what the newfound relations mean or why they hold.

This is a post singularity-type scenario, in which the machines make discoveries at a rate that humans cannot keep up with. Of course, astronomers are not the only scientists with this fate in store. But as the ones who have more or less automated their jobs already, they’re likely to be the ones who come up against it first.

MIT researchers have developed a new endoscopy technology that could make it easier for doctors to detect precancerous lesions in the colon. Early detection of such lesions has been shown to reduce death rates from colorectal cancer, which kills about 50,000 people per year in the United States.

The new technique, known as photometric stereo endoscopy, can capture topographical images of the colon surface along with traditional two-dimensional images. Such images make it easier to see precancerous growths, including flatter lesions that traditional endoscopy usually misses, says Nicholas Durr, a research fellow in the Madrid-MIT M+Vision Consortium, a recently formed community of medical researchers in Boston and Madrid.

“In conventional colonoscopy screening, you look for these characteristic large polyps that grow into the lumen of the colon, which are relatively easy to see,” Durr says. “However, a lot of studies in the last few years have shown that more subtle, nonpolypoid lesions can also cause cancer.”

In the United States, colonoscopies are recommended beginning at age 50, and are credited with reducing the risk of death from colorectal cancer by about half. Traditional colonoscopy uses endoscopes with fiber-optic cameras to capture images.

Durr and his colleagues, seeking medical problems that could be solved with new optical technology, realized that there was a need to detect lesions that colonoscopy can miss. A technique called chromoendoscopy, in which a dye is sprayed in the colon to highlight topographical changes, offers better sensitivity but is not routinely used because it takes too long.

“What is attractive about this technique for colonoscopy is that it provides an added dimension of diagnostic information, particularly about three-dimensional morphology on the surface of the colon,” says Nimmi Ramanujam, a professor of biological engineering at Duke University who was not part of the research team.

The researchers built two prototypes — one 35 millimeters in diameter, which would be too large to use for colonoscopy, and one 14 millimeters in diameter, the size of a typical colonoscope. In tests with an artificial silicon colon, the researchers found that both prototypes could create 3-D representations of polyps and flatter lesions.

The new technology should be easily incorporated into newer endoscopes, Durr says. “A lot of existing colonoscopes already have multiple light sources,” he says. “From a hardware perspective all they need to do is alternate the lights and then update their software to process this photometric data.”

The researchers plan to test the technology in human patients in clinical trials at MGH and the Hospital Clinico San Carlos in Madrid. They are also working on additional computer algorithms that could help to automate the process of identifying polyps and lesions from the topographical information generated by the new system.

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"Adoptive T cell transfer for cancer and chronic infection is an emerging field that shows promise in recent trials. Synthetic-biology-based engineering of T lymphocytes to express high-affinity antigen receptors can overcome immune tolerance, which has been a major limitation of immunotherapy-based strategies. Advances in cell engineering and culture approaches to enable efficient gene transfer and ex vivo cell expansion have facilitated broader evaluation of this technology, moving adoptive transfer from a “boutique” application to the cusp of a mainstream technology. The major challenge currently facing the field is to increase the specificity of engineered T cells for tumors, because targeting shared antigens has the potential to lead to on-target off-tumor toxicities, as observed in recent trials. As the field of adoptive transfer technology matures, the major engineering challenge is the development of automated cell culture systems, so that the approach can extend beyond specialized academic centers and become widely available."

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